My name is Anderson Monken. I am a Data Science Manager and an Adjunct Professor of Data Science.

I specialize in artificial intelligence, big data, and natural language processing. My professional interest is in the management and application of artificial intelligence to responsibly improve policymaking and government effectiveness.

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Group Manager

International Finance Division - Federal Reserve Board of Governors

Feb 2022 – Present Washington, D.C.

I am the inaugural manager for the Data Science and Application Development (DSAD) group in the International Finance Division. The team serves as in-house experts in application design, UX design, robotic process automation, data science, big data, and artificial intelligence for research, policy, and operational needs at the Federal Reserve Board.

I lead a team of 5 full-time data scientists and application developers who manage application and data products that serve hundreds of internal staff. We contribute to the Board’s cloud adoption, artificial intelligence maturity development, and maintain several important climate, textual, and trade databases.

In the Fall 2022 and Spring 2023 semesters, I served as the Instructor of Record for the Howard University course run by the Federal Reserve Board on statistical programming and economic literacy.


Adjunct Professor

Data Science and Analytics Program - Georgetown University

Jun 2021 – Present Washington, D.C.
Selected for expertise in big data, cloud computing, and data science. In this role, I teach graduate students skills in machine learning, data science, and big data. My personal faculty website can be found here -

Technology Analyst

International Finance Division - Federal Reserve Board of Governors

Oct 2018 – Feb 2022 Washington, D.C.

Responsibilities included:

  • Research on international trade using machine learning and artificial intelligence
  • PySpark textual analysis on big data
  • Business-to-infrastructure problem solving
  • Hadoop database management, data wrangling, and education to staff
  • Staff education programs on data science and machine learning topics
  • Leadership in community of practice groups for R and cloud

Senior Research Assistant

International Finance Division - Federal Reserve Board of Governors

Jul 2018 – Sep 2018 Washington, D.C.


  • Upgraded macroeconomic forecasting architecture
  • Researched developments in labor force participation in advanced economies
  • Forecasted economic growth indicators (i.e., GDP, CPI, interest rates, output gap) for Sweden, Norway, and Denmark, as well as wrote country briefing notes detailing economic conditions

Research Assistant

International Finance Division - Federal Reserve Board of Governors

Jun 2017 – Jul 2018 Washington, D.C.


  • Initiated overhaul of daily data update programs using robotic process automation to reduce errors and improve user-friendliness using a combination of Stata, FAME, HTML, and Linux
  • Designed an automated data release alert program using Python
  • Led RA team to produce division’s first all R-chart briefing and assisted in beta-testing IF Functions package
  • Prepared and analyzed data to support a variety of policy and economic research projects


Special Achievement Award

Award for extraordinary initiative and innovation in advancing the Board’s data management and analytics capabilities. I worked across the Board and Federal Reserve System to promote innovative techniques and new technologies to change how analysts do their work. My efforts have made big-data analysis, machine learning, artificial intelligence, text analysis, and cloud computing more accessible to other staff, enabling these techniques to become integral parts of regular processes.

Master of Science - Data Science and Analytics

Developed skills in data science, machine learning, and deep learning.

  • GPA 4.0/4.0
  • Teaching Assistant for ANLY 501/502/511/512

Bachelor of Arts

Majors in Chemistry, Economics, and Mathematics

  • Highest Honors in Economics
  • Honors in Chemistry
  • Phi Beta Kappa
  • GPA 3.9 / 4.0

Recent Publications

Highlighting the methods needed to assure that ML and AI models are explainable, fair, and bias-free.

Demonstrating relationship between textual analytics sentiment and output prices during global supply chain bottlenecks.

Harnessing big data techniques to analyze bill of lading data for timely international trade indicators.

Novel graph neural network method to model worldwide trade in the context of a network graph to predict trade unit value.

Ensemble machine learning and association rules mining to improve agricultural trade during outlier events.


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